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Machine learning for NLP 2

  • ECTS

    6 crédits



The course provides the fundamental concepts of supervised classification via deep learning methods, with typical examples in NLP.

1. General concepts for supervised classification

  • methodology
  • evaluation metrics

2. A first classifier : k-NN

3. Linear and log-linear models

  • linear separability
  • prediction with a (log-)linear classifier
  • perceptron learning algorithm
  • kernel methods
  • logistic regression

4. Extension to Multi-layer perceptrons

  • non linearity
  • fully connected feed-forward neural network
  • universal approximation theorem

5. Learning as loss minimization

  • usual loss functions
  • stochastic gradient descent
  • backpropagation algorithm

6. vectorial representations

  • word embeddings as dense features
  • word embeddings learning

Lab sessions will illustrate the course, introducing in particular:

  • tensor manipulation in numpy / pytorch
  • sklearn and pytorch libraries


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